AQME: Automated quantum mechanical environments for researchers and educators

Abstract

AQME, automated quantum mechanical environments, is a free and open‐source Python package for the rapid deployment of automated workflows using cheminformatics and quantum chemistry. AQME workflows integrate tasks performed across multiple computational chemistry packages and data formats, preserving all computational protocols, data, and metadata for machine and human users to access and reuse. AQME has a modular structure of independent modules that can be implemented in any sequence, allowing the users to use all or only the desired parts of the program. The code has been developed for researchers with basic familiarity with the Python programming language. The CSEARCH module interfaces to molecular mechanics and semi‐empirical QM (SQM) conformer generation tools (e.g., RDKit and Conformer–Rotamer Ensemble Sampling Tool, CREST) starting from various initial structure formats. The CMIN module enables geometry refinement with SQM and neural network potentials, such as ANI. The QPREP module interfaces with multiple QM programs, such as Gaussian, ORCA, and PySCF. The QCORR module processes QM results, storing structural, energetic, and property data while also enabling automated error handling (i.e., convergence errors, wrong number of imaginary frequencies, isomerization, etc.) and job resubmission. The QDESCP module provides easy access to QM ensemble‐averaged molecular descriptors and computed properties, such as NMR spectra. Overall, AQME provides automated, transparent, and reproducible workflows to produce, analyze and archive computational chemistry results. SMILES inputs can be used, and many aspects of tedious human manipulation can be avoided. Installation and execution on Windows, macOS, and Linux platforms have been tested, and the code has been developed to support access through Jupyter Notebooks, the command line, and job submission (e.g., Slurm) scripts. Examples of pre‐configured workflows are available in various formats, and hands‐on video tutorials illustrate their use.This article is categorized under: Data Science > Chemoinformatics Data Science > Computer Algorithms and Programming Software > Quantum Chemistry

Understanding chiral proton organocatalysis using cinchonium derivatives

“…Due to the differences in energy observed when varying the level of theory, we opted to include various DFT and wavefunction theory methods. [33][34][35] Figure 2 displays the average values and standard deviations of six combinations of widely-used functionals and basis sets in modern mechanistic studies (methods A to F). A priori, it was not possible to select a single method from the six available based on their benchmarking against experimental results (Tables S4-5).…”

GUIDE: A GUI for automated quantum chemistry calculations

“…GUIDE enables users to perform QM calculations at the PM6 and PM7 12 levels without using command lines. Some GUIs for quantum chemical calculations, such as Chemshell, 13 Avogadro, 14 AQME, 15 Open Babel, 16 and GAUSSIAN View, 17 are already available. However, these tools may be limited in accessibility due to their availability in specific versions for certain operating systems or their requirement for specialized hardware to run.…”

Protein3D: Enabling analysis and extraction of metal‐containing sites from the Protein Data Bank with molSimplify

“…Computational screening is frequently performed to design biomimetic transition metal complexes that can mimic properties of metalloenzyme active sites (e.g., metalloporphyrins 37,38 ), but often lack selectivity and the rate enhancement of biological catalysts. The study and analysis of transition metal complexes is streamlined with tools [39][40][41][42][43][44][45][46] such as molSimplify, 39 molassembler, 40 and DENOPTIM, 42 available to assist in construction of inorganic complexes and analysis. In this work, we extend molSimplify to the analysis of metalloproteins by creation of the protein3D class.…”